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train_SAM.py
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train_SAM.py
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#coding:utf-8
import torch
import torch.nn as nn
from torch.optim.lr_scheduler import _LRScheduler
from torch.utils.data import DataLoader
import torch.optim as opt
import os
import time
import argparse
import datetime
import numpy as np
from tqdm import tqdm
import shutil
from model.model_proxy_SAM import SonarSAM, ModelWithLoss
from evaluate import evaluate
from utils.config import Config_SAM
from utils.logger import Logger
from utils.utils import rand_seed
from dataloader.data_loader import DebrisDataset, collate_fn_seq
from model.segment_anything.utils.transforms import ResizeLongestSide
class WarmUpLR(_LRScheduler):
"""warmup_training learning rate scheduler
Args:
optimizer: optimzier(e.g. SGD)
total_iters: totoal_iters of warmup phase
"""
def __init__(self, optimizer, total_iters, last_epoch=-1):
self.total_iters = total_iters
super().__init__(optimizer, last_epoch)
def get_lr(self):
"""we will use the first m batches, and set the learning
rate to base_lr * m / total_iters
"""
return [base_lr * self.last_epoch / (self.total_iters + 1e-8) for base_lr in self.base_lrs]
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--config", type=str, help="config path (*.yaml)", required=True)
parser.add_argument("--save_path", type=str, help="save path", default='')
args = parser.parse_args()
opt = Config_SAM(config_path=args.config)
rand_seed(opt.RANDOM_SEED)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# log & model folder
if args.save_path == '':
opt.MODEL_DIR += '{}_{}'.format(opt.MODEL_NAME, datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
else:
opt.MODEL_DIR = args.save_path
if not os.path.exists(opt.MODEL_DIR):
os.mkdir(opt.MODEL_DIR)
logger = Logger(opt.MODEL_NAME, path=opt.MODEL_DIR)
if not os.path.exists(os.path.join(opt.MODEL_DIR, 'params.yaml')):
shutil.copy(args.config, os.path.join(opt.MODEL_DIR, 'params.yaml'))
# dataset
train_dataset = DebrisDataset(root_path=opt.DATA_PATH, image_list=os.path.join(opt.IMAGE_LIST_PATH, 'train.txt'),
input_size=opt.INPUT_SIZE, use_augment=True)
val_dataset = DebrisDataset(root_path=opt.DATA_PATH, image_list=os.path.join(opt.IMAGE_LIST_PATH, 'val.txt'),
input_size=opt.INPUT_SIZE, use_augment=False)
train_loader = DataLoader(train_dataset, batch_size=opt.TRAIN_BATCHSIZE, shuffle=True,
num_workers=opt.TRAIN_BATCHSIZE, collate_fn=collate_fn_seq)
val_loader = DataLoader(val_dataset, batch_size=opt.VAL_BATCHSIZE, shuffle=False,
num_workers=opt.VAL_BATCHSIZE*2, collate_fn=collate_fn_seq)
rand_seed(opt.RANDOM_SEED)
# Training Config
epochs = opt.EPOCH_NUM
epoch_start = 0
net = SonarSAM(model_name=opt.SAM_NAME, checkpoint=opt.SAM_CHECKPOINT, num_classes=opt.OUTPUT_CHN,
is_finetune_image_encoder=opt.IS_FINETUNE_IMAGE_ENCODER,
use_adaptation=opt.USE_ADAPTATION,
adaptation_type=opt.ADAPTATION_TYPE,
head_type=opt.HEAD_TYPE,
reduction=4, upsample_times=2, groups=4)
net = ModelWithLoss(net)
if opt.OPTIMIZER == 'ADAM':
optimizer = torch.optim.Adam(
net.parameters(), lr=opt.LEARNING_RATE, weight_decay=opt.WEIGHT_DECAY)
else:
optimizer = torch.optim.SGD(
net.parameters(), lr=opt.LEARNING_RATE, momentum=opt.MOMENTUM,
weight_decay=opt.WEIGHT_DECAY, nesterov=True)
warmup_scheduler = WarmUpLR(optimizer, len(train_loader) * opt.WARM_LEN)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs)
# Resume
if opt.RESUME_FROM > 0:
ckpt = torch.load(
os.path.join(opt.MODEL_DIR, '{}_{}.pth'.format(opt.MODEL_NAME, opt.RESUME_FROM)))
net.load_state_dict(ckpt['state_dict'])
if 'optimizer' in ckpt.keys():
optimizer.load_state_dict(ckpt['optimizer'])
if 'scheduler' in ckpt.keys():
scheduler.load_state_dict(ckpt['scheduler'])
epoch_start = opt.RESUME_FROM
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
net = nn.DataParallel(net)
net.to(device)
best_score = 0
# Training
for epoch in range(epoch_start, epochs):
net.train()
print_str = '-------epoch {}/{}-------'.format(epoch+1, epochs)
logger.write_and_print(print_str)
start_t = time.time()
for step, (image, mask, boxes) in enumerate(tqdm(train_loader)):
image = image.to(device)
mask = mask.to(device)
# print('image shape', patch.shape)
# print('GT shape', mask.shape)
# prepare data
optimizer.zero_grad()
boxes = None
loss, outputs = net.forward(image, mask, boxes=boxes)
if torch.cuda.device_count() > 1:
loss = loss.sum()
if torch.isnan(loss):
logger.write_and_print('***** Warning: loss is NaN *****')
loss = torch.tensor(10000).to(device)
# print loss
print_str = '# total loss: {}\n'.format(loss.item())
if opt.PRT_LOSS:
logger.write_and_print(print_str)
else:
logger.write(print_str)
loss.backward()
optimizer.step()
if epoch <= opt.WARM_LEN:
warmup_scheduler.step()
# print('lr', optimizer.param_groups[0]['lr'])
end_t = time.time()
duration = end_t-start_t
logger.write_and_print('time: {}'.format(duration))
# log learning_rate
current_lr = optimizer.param_groups[0]['lr']
scheduler.step()
# save model
if torch.cuda.device_count() > 1:
ckpt = {
'epoch': epoch + 1,
'state_dict': net.module.state_dict(),
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
}
else:
ckpt = {
'epoch': epoch + 1,
'state_dict': net.state_dict(),
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
}
torch.save(ckpt, os.path.join(opt.MODEL_DIR,
'{}_{}.pth'.format(opt.MODEL_NAME, epoch+1)))
# evaluate each epoch
if torch.cuda.device_count() > 1:
metrics_dict = evaluate(net.module.model, val_loader, device, opt)
else:
metrics_dict = evaluate(net.model, val_loader, device, opt)
if metrics_dict['avg(exclude_bg)'] > best_score:
best_score = metrics_dict['avg(exclude_bg)']
torch.save(ckpt, os.path.join(opt.MODEL_DIR, '{}_best.pth'.format(opt.MODEL_NAME)))
print_str = ''
for key in metrics_dict.keys():
print_str += key + ':\t{:.2f}\n'.format(metrics_dict[key]*100)
logger.write_and_print(print_str)
# evaluate final
test_dataset = DebrisDataset(root_path=opt.DATA_PATH, image_list=os.path.join(opt.IMAGE_LIST_PATH, 'test.txt'),
input_size=opt.INPUT_SIZE, use_augment=False)
test_loader = DataLoader(test_dataset, batch_size=opt.VAL_BATCHSIZE, shuffle=False,
num_workers=opt.VAL_BATCHSIZE*2, collate_fn=collate_fn_seq)
ckpt = torch.load(
os.path.join(opt.MODEL_DIR, '{}_best.pth'.format(opt.MODEL_NAME)))
net = SonarSAM(model_name=opt.SAM_NAME, checkpoint=opt.SAM_CHECKPOINT, num_classes=opt.OUTPUT_CHN,
is_finetune_image_encoder=opt.IS_FINETUNE_IMAGE_ENCODER,
use_adaptation=opt.USE_ADAPTATION, adaptation_type=opt.ADAPTATION_TYPE,
head_type=opt.HEAD_TYPE,
reduction=4, upsample_times=2, groups=4)
net = ModelWithLoss(net)
net.load_state_dict(ckpt['state_dict'])
if torch.cuda.device_count() > 1:
net = nn.DataParallel(net)
net.to(device)
rand_seed(opt.RANDOM_SEED)
if torch.cuda.device_count() > 1:
metrics_dict = evaluate(net.module.model, test_loader, device, opt)
else:
metrics_dict = evaluate(net.model, test_loader, device, opt)
logger.write_and_print("Dice on Test set:")
for key in metrics_dict.keys():
logger.write_and_print("{}:\t{:.2f}".format(key, metrics_dict[key]*100))
if __name__ == "__main__":
torch.autograd.set_detect_anomaly(True)
main()